{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入类库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from numpy import arange\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import set_option\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入数据\n",
    "filename = 'housing.csv'\n",
    "names = ['CRIM','ZN','INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PRTATIO','B','LSTAT','MEDV']\n",
    "dataset = pd.read_csv(filename,names= names, delim_whitespace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 14)\n"
     ]
    }
   ],
   "source": [
    "# 数据维度\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRIM       float64\n",
      "ZN         float64\n",
      "INDUS      float64\n",
      "CHAS         int64\n",
      "NOX        float64\n",
      "RM         float64\n",
      "AGE        float64\n",
      "DIS        float64\n",
      "RAD          int64\n",
      "TAX        float64\n",
      "PRTATIO    float64\n",
      "B          float64\n",
      "LSTAT      float64\n",
      "MEDV       float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 特征属性的字段类型\n",
    "print(dataset.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          CRIM     ZN  INDUS     CHAS    NOX     RM    AGE    DIS    RAD  \\\n",
      "count  5.1e+02  506.0  506.0  5.1e+02  506.0  506.0  506.0  506.0  506.0   \n",
      "mean   3.6e+00   11.4   11.1  6.9e-02    0.6    6.3   68.6    3.8    9.5   \n",
      "std    8.6e+00   23.3    6.9  2.5e-01    0.1    0.7   28.1    2.1    8.7   \n",
      "min    6.3e-03    0.0    0.5  0.0e+00    0.4    3.6    2.9    1.1    1.0   \n",
      "25%    8.2e-02    0.0    5.2  0.0e+00    0.4    5.9   45.0    2.1    4.0   \n",
      "50%    2.6e-01    0.0    9.7  0.0e+00    0.5    6.2   77.5    3.2    5.0   \n",
      "75%    3.7e+00   12.5   18.1  0.0e+00    0.6    6.6   94.1    5.2   24.0   \n",
      "max    8.9e+01  100.0   27.7  1.0e+00    0.9    8.8  100.0   12.1   24.0   \n",
      "\n",
      "         TAX  PRTATIO      B  LSTAT   MEDV  \n",
      "count  506.0    506.0  506.0  506.0  506.0  \n",
      "mean   408.2     18.5  356.7   12.7   22.5  \n",
      "std    168.5      2.2   91.3    7.1    9.2  \n",
      "min    187.0     12.6    0.3    1.7    5.0  \n",
      "25%    279.0     17.4  375.4    6.9   17.0  \n",
      "50%    330.0     19.1  391.4   11.4   21.2  \n",
      "75%    666.0     20.2  396.2   17.0   25.0  \n",
      "max    711.0     22.0  396.9   38.0   50.0  \n"
     ]
    }
   ],
   "source": [
    "# 描述统计信息\n",
    "pd.set_option('precision',1)\n",
    "print(dataset.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         CRIM    ZN  INDUS      CHAS   NOX    RM   AGE   DIS       RAD   TAX  \\\n",
      "CRIM     1.00 -0.20   0.41 -5.59e-02  0.42 -0.22  0.35 -0.38  6.26e-01  0.58   \n",
      "ZN      -0.20  1.00  -0.53 -4.27e-02 -0.52  0.31 -0.57  0.66 -3.12e-01 -0.31   \n",
      "INDUS    0.41 -0.53   1.00  6.29e-02  0.76 -0.39  0.64 -0.71  5.95e-01  0.72   \n",
      "CHAS    -0.06 -0.04   0.06  1.00e+00  0.09  0.09  0.09 -0.10 -7.37e-03 -0.04   \n",
      "NOX      0.42 -0.52   0.76  9.12e-02  1.00 -0.30  0.73 -0.77  6.11e-01  0.67   \n",
      "RM      -0.22  0.31  -0.39  9.13e-02 -0.30  1.00 -0.24  0.21 -2.10e-01 -0.29   \n",
      "AGE      0.35 -0.57   0.64  8.65e-02  0.73 -0.24  1.00 -0.75  4.56e-01  0.51   \n",
      "DIS     -0.38  0.66  -0.71 -9.92e-02 -0.77  0.21 -0.75  1.00 -4.95e-01 -0.53   \n",
      "RAD      0.63 -0.31   0.60 -7.37e-03  0.61 -0.21  0.46 -0.49  1.00e+00  0.91   \n",
      "TAX      0.58 -0.31   0.72 -3.56e-02  0.67 -0.29  0.51 -0.53  9.10e-01  1.00   \n",
      "PRTATIO  0.29 -0.39   0.38 -1.22e-01  0.19 -0.36  0.26 -0.23  4.65e-01  0.46   \n",
      "B       -0.39  0.18  -0.36  4.88e-02 -0.38  0.13 -0.27  0.29 -4.44e-01 -0.44   \n",
      "LSTAT    0.46 -0.41   0.60 -5.39e-02  0.59 -0.61  0.60 -0.50  4.89e-01  0.54   \n",
      "MEDV    -0.39  0.36  -0.48  1.75e-01 -0.43  0.70 -0.38  0.25 -3.82e-01 -0.47   \n",
      "\n",
      "         PRTATIO     B  LSTAT  MEDV  \n",
      "CRIM        0.29 -0.39   0.46 -0.39  \n",
      "ZN         -0.39  0.18  -0.41  0.36  \n",
      "INDUS       0.38 -0.36   0.60 -0.48  \n",
      "CHAS       -0.12  0.05  -0.05  0.18  \n",
      "NOX         0.19 -0.38   0.59 -0.43  \n",
      "RM         -0.36  0.13  -0.61  0.70  \n",
      "AGE         0.26 -0.27   0.60 -0.38  \n",
      "DIS        -0.23  0.29  -0.50  0.25  \n",
      "RAD         0.46 -0.44   0.49 -0.38  \n",
      "TAX         0.46 -0.44   0.54 -0.47  \n",
      "PRTATIO     1.00 -0.18   0.37 -0.51  \n",
      "B          -0.18  1.00  -0.37  0.33  \n",
      "LSTAT       0.37 -0.37   1.00 -0.74  \n",
      "MEDV       -0.51  0.33  -0.74  1.00  \n"
     ]
    }
   ],
   "source": [
    "# 关联关系\n",
    "pd.set_option('precision',2)\n",
    "print(dataset.corr(method='pearson'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直方图\n",
    "dataset.hist(sharex=False,sharey=False,xlabelsize=1,ylabelsize=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 密度图\n",
    "dataset.plot(kind = 'density', subplots = True, layout = (4,4),sharex = False, fontsize =1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 箱线图\n",
    "dataset.plot(kind ='box', subplots = True, layout = (4,4),sharex = False, sharey = False,fontsize = 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 196 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散点矩阵图\n",
    "scatter_matrix(dataset)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 相关矩阵图\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(dataset.corr(),vmin = -1, vmax = 1, interpolation = 'none')\n",
    "fig.colorbar(cax)\n",
    "ticks = np.arange(0,14,1)\n",
    "ax.set_xticks(ticks)\n",
    "ax.set_yticks(ticks)\n",
    "ax.set_xticklabels(names)\n",
    "ax.set_yticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分离数据集\n",
    "array = dataset.values\n",
    "X = array[:, 0:13]\n",
    "Y = array[:, 13]\n",
    "validation_size = 0.2\n",
    "seed = 7\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X, Y, test_size=validation_size, random_state= seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 评估算法 —— 评估标准\n",
    "num_folds = 10\n",
    "seed = 7\n",
    "scoring = 'neg_mean_squared_error'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 评估算法 —— baseline\n",
    "models = {}\n",
    "models['LR'] = LinearRegression()\n",
    "models['LASSO'] = Lasso()\n",
    "models['EN'] = ElasticNet()\n",
    "models['KNN'] = KNeighborsRegressor()\n",
    "models['CART'] =DecisionTreeRegressor()\n",
    "models['SVM'] = SVR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: -21.379856(9.414264)\n",
      "LASSO: -26.423561(11.651110)\n",
      "EN: -27.502259(12.305022)\n",
      "KNN: -41.896488(13.901688)\n",
      "CART: -25.947784(12.703007)\n",
      "SVM: -67.827886(29.049138)\n"
     ]
    }
   ],
   "source": [
    "# 评估算法\n",
    "results = []\n",
    "for key in models:\n",
    "    kfold = KFold(n_splits=num_folds,random_state=seed)\n",
    "    cv_result = cross_val_score(models[key],X_train,Y_train,cv = kfold,scoring=scoring)\n",
    "    results.append(cv_result)\n",
    "    print('%s: %f(%f)'%(key,cv_result.mean(),cv_result.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 评估算法 —— 箱线图\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(models.keys())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: -21.379856(9.414264)\n",
      "LASSO: -26.423561(11.651110)\n",
      "EN: -27.502259(12.305022)\n",
      "KNN: -41.896488(13.901688)\n",
      "CART: -25.647641(13.692306)\n",
      "SVM: -67.827886(29.049138)\n"
     ]
    }
   ],
   "source": [
    "# 评估算法 —— 正太化数据\n",
    "pipelines = {}\n",
    "pipelines['ScalerLR'] = Pipeline([('Scaler',StandardScaler()),('LR',LinearRegression())])\n",
    "pipelines['ScalerLASSO'] = Pipeline([('Scaler',StandardScaler()),('LASSO',Lasso())])\n",
    "pipelines['SclaerEn'] = Pipeline([('Scaler',StandardScaler()),('EN',ElasticNet())])\n",
    "pipelines['ScalerKNN'] = Pipeline([('Scaler',StandardScaler()),('KNN',KNeighborsRegressor())])\n",
    "pipelines['ScalerCART'] = Pipeline([('Scaler',StandardScaler()),('CART',DecisionTreeRegressor())])\n",
    "pipelines['ScalerSVM'] = Pipeline([('Scaler',StandardScaler()),('SVM',SVR())])\n",
    "results = []\n",
    "for key in models:\n",
    "    kfold = KFold(n_splits=num_folds,random_state=seed)\n",
    "    cv_result = cross_val_score(models[key],X_train,Y_train,cv = kfold,scoring=scoring)\n",
    "    results.append(cv_result)\n",
    "    print('%s: %f(%f)'%(key,cv_result.mean(),cv_result.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 评估算法 —— 箱线图\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(models.keys())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最优：-18.10930386178861 使用{'n_neighbors': 3}\n",
      "-20.169640(14.986904) with {'n_neighbors': 1}\n",
      "-18.109304(12.880861) with {'n_neighbors': 3}\n",
      "-20.063115(12.138331) with {'n_neighbors': 5}\n",
      "-20.514297(12.278136) with {'n_neighbors': 7}\n",
      "-20.319536(11.554509) with {'n_neighbors': 9}\n",
      "-20.963145(11.540907) with {'n_neighbors': 11}\n",
      "-21.099040(11.870962) with {'n_neighbors': 13}\n",
      "-21.506843(11.468311) with {'n_neighbors': 15}\n",
      "-22.739137(11.499596) with {'n_neighbors': 17}\n",
      "-23.829011(11.277558) with {'n_neighbors': 19}\n",
      "-24.320892(11.849667) with {'n_neighbors': 21}\n"
     ]
    }
   ],
   "source": [
    "# 调参改善算法 —— KNN\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "rescaledX = scaler.transform(X_train)\n",
    "param_grid = {'n_neighbors':[1,3,5,7,9,11,13,15,17,19,21]}\n",
    "model = KNeighborsRegressor()\n",
    "kfold = KFold(n_splits=num_folds,random_state=seed)\n",
    "grid = GridSearchCV(estimator=model, param_grid = param_grid, scoring=scoring,cv=kfold)\n",
    "grid_result = grid.fit(X = rescaledX, y=Y_train)\n",
    "print('最优：%s 使用%s'%(grid_result.best_score_,grid_result.best_params_))\n",
    "cv_results = zip(grid_result.cv_results_['mean_test_score'],\n",
    "                grid_result.cv_results_['std_test_score'],\n",
    "                grid_result.cv_results_['params'])\n",
    "for mean, std, param in cv_results :\n",
    "    print('%f(%f) with %r'%(mean,std,param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ScaledAB: -14.605052(6.455070)\n",
      "ScaledAB-KNN: -15.518512(9.689451)\n",
      "ScaledAB-LR: -24.416240(9.079068)\n",
      "ScaledRFR: -11.926645(6.242848)\n",
      "ScaledETR: -9.429135(5.942827)\n",
      "ScaledGBR: -10.057371(4.490587)\n"
     ]
    }
   ],
   "source": [
    "# 集成算法\n",
    "ensembles = {}\n",
    "ensembles['ScaledAB'] = Pipeline([('Scaler',StandardScaler()),('AB',AdaBoostRegressor())])\n",
    "ensembles['ScaledAB-KNN'] = Pipeline([('Scaler',StandardScaler()),('ABKNN',AdaBoostRegressor(base_estimator=KNeighborsRegressor(n_neighbors=3)))])\n",
    "ensembles['ScaledAB-LR'] = Pipeline([('Scaler',StandardScaler()),('ABLR',AdaBoostRegressor(LinearRegression()))])\n",
    "ensembles['ScaledRFR'] = Pipeline([('Scaler',StandardScaler()),('RFR',RandomForestRegressor())])\n",
    "ensembles['ScaledETR'] = Pipeline([('Scaler',StandardScaler()),('ETR',ExtraTreesRegressor())])\n",
    "ensembles['ScaledGBR'] = Pipeline([('Scaler',StandardScaler()),('GBR',GradientBoostingRegressor())])\n",
    "\n",
    "results = []\n",
    "for key in ensembles:\n",
    "    kfold = KFold(n_splits=num_folds,random_state=seed)\n",
    "    cv_result = cross_val_score(ensembles[key],X_train,Y_train,cv=kfold,scoring=scoring)\n",
    "    results.append(cv_result)\n",
    "    print('%s: %f(%f)'%(key,cv_result.mean(),cv_result.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 集成算法 —— 箱线图\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(ensembles.keys())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最优：-18.10930386178861 使用{'n_neighbors': 3}\n",
      "最优：-9.06331904487805 使用{'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "# 集成算法GBM —— 调参\n",
    "caler = StandardScaler().fit(X_train)\n",
    "rescaledX = scaler.transform(X_train)\n",
    "param_grid = {'n_estimators':[10,50,100,200,300,400,500,600,700,800,900]}\n",
    "model = GradientBoostingRegressor()\n",
    "kfold = GridSearchCV(estimator=model,param_grid=param_grid,scoring=scoring,cv= kfold)\n",
    "grid_result = grid.fit(X = rescaledX, y = Y_train)\n",
    "print('最优：%s 使用%s'%(grid_result.best_score_,grid_result.best_params_))\n",
    "\n",
    "# 集成算法ET —— 调参\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "rescaledX = scaler.transform(X_train)\n",
    "param_grid = {'n_estimators':[5,10,20,30,40,50,60,70,80,90,100]}\n",
    "model = ExtraTreesRegressor()\n",
    "kfold = KFold(n_splits=num_folds,random_state=seed)\n",
    "grid = GridSearchCV(estimator=model,param_grid = param_grid,scoring=scoring,cv=kfold)\n",
    "grid_result = grid.fit(X = rescaledX,y =Y_train)\n",
    "print('最优：%s 使用%s'%(grid_result.best_score_,grid_result.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(n_estimators=80)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练模型\n",
    "caler = StandardScaler().fit(X_train)\n",
    "rescaledX = scaler.transform(X_train)\n",
    "gbr = ExtraTreesRegressor(n_estimators=80)\n",
    "gbr.fit(X = rescaledX,y = Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.222926501225487\n"
     ]
    }
   ],
   "source": [
    "# 评估算法模型\n",
    "rescaledX_validation = scaler.transform(X_validation)\n",
    "predictions = gbr.predict(rescaledX_validation)\n",
    "print(mean_squared_error( Y_validation,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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